Transferability and imperceptibility of adversarial examples are pivotal for assessing the efficacy of black-box attacks. While diffusion models have been employed to generate adversarial examples, leveraging their advanced image generation capability to enhance transferability and imperceptibility, these methods typically focus only on perturbing the image or latent space. They often ignore the critical role of semantic information in the denoising process, thereby impeding the improvement of the transferability of adversarial examples. Furthermore, the modification of high-level semantics inevitably introduces image blurring. This degradation in visual quality makes the adversarial examples more susceptible to detection. To overcome the above limitations, we are the first to utilize image latent encoding and semantic embedding perturbations to enhance the performance of adversarial attacks. Then, the LESEP method is proposed. In the LESEP framework, we first apply image latent encoding attack to achieve deception of the target model. Second, the semantic embedding attack enhances the transferability of adversarial examples. Additionally, we utilize the image restoration technique to guarantee the high imperceptibility of the crafted adversarial examples. Through comprehensive experiments on diverse datasets, different network architectures and defense methods, we have demonstrated that the LESEP method achieves outstanding transferability and imperceptibility while displaying strong robustness.

In this paper, we present a novel task of source-free cross-modal adversarial example generation, which generates adversarial examples based on textual descriptions of attackers. This task has two challenges as follows. First, how to generate adversarial examples when the clean examples are missing or inaccessible. Second, how to achieve fine-grained custom adversarial example generation according to the semantic descriptions of the attackers. Existing adversarial example generation methods can not effectively deal with these two challenges. To address these challenges, we propose a Source-Free Cross-Modal Adversarial Example Generation framework, abbreviated as SFCM-AEG. Within the SFCM-AEG model, we firstly leverage a pre-trained GPT as a simulator to construct textual descriptions of attackers by labels. Following this, we employ a diffusion model to synthesize an image that aligns with the generated textual description. Finally, the generated images are converted into adversarial examples using an adversarial example generation method. Experimental results demonstrate that our proposed SFCM-AEG method can generate adversarial examples with customized semantic descriptions, without relying on clean examples, while achieving strong attack performance in a white-box setting.

Federated learning enables multiple data providers to collaborate on training models without exposing personal data. During the training process, frequent communication is required between the data provider and the central server, which puts great pressure on federated learning. To reduce the communication pressure of federated learning, we use the CP-decomposition processing model to reduce the size of data that needs to be transmitted during the communication process. In addition, we aggregate the global model based on the Shapley value, and eliminate nodes that are not beneficial to federated learning as soon as possible, which reduces the communication pressure and can stimulate the participating nodes and enhance the enthusiasm of participants in federated learning, thus improving the training results of the global model. We named the system CPSV, which stands for Federated learning of CP-decomposition models based on Shapley value aggregation. Numerous experiments on CPSV have shown that CPSV can motivate and supervise participating nodes to aggregate better global models while reducing the stress of federal learning communication.